The Intel® Distribution of OpenVINO™ toolkit includes two sets of optimized models that can expedite development and improve image processing pipelines for Intel® processors. Use these models for development and production deployment without the need to search for or to train your own models.

Public Model Set

Download and incorporate some of the most popular models created by the open developer community using the included Model Downloader. Add these models directly to your environment and accelerate your development.

Find the downloader in this toolkit folder: \deployment_tools\tools\model_downloader.

Free Model Set

Discover the capabilities of Intel® software and silicon with a fully functioning set of pretrained models. These models provide common vision use cases and reduce development time and cost. Documentation for each model includes links to public data.

For more details on the complete list of pretrained models included in the package, see Documentation.

Enhance the input image resolution by a factor of four or three with single-image, super resolution networks that are built on this approach. The two models are faster than the SRResNet-based networks and have better memory consumption.

While similar to the standard model, this model performs better in a wider range of lighting conditions. The detector backbone is SqueezeNet light (half-channels) with a single-shot detector (SSD) for shooting indoor and outdoor scenes with a front-facing camera.

Different colored bounding boxes simultaneously detect a head and an entire person. Based on a backbone similar to MobileNetV2, the model includes depth-wise convolutions that reduce computation for a 3 x 3 convolution block.

This multiperson, 2D pose estimation network is based on the OpenPose approach and uses a tuned MobileNetV1 to extract features. It detects a skeleton (which consists of keypoints and connections between them) to identify human poses for every person inside the image. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles.

Conduct an initial analysis and present back-key metadata for faster sorting and searching in the future. The average color accuracy for the model is over 82 percent for red, white, black, green, yellow, gray, and blue. Its average vehicle-type attribution is over 87 percent for cars, vans, trucks, and buses.